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Journal articles on the topic 'Community composition'

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Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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1

Blayda, I. A. "COMPOSITION AND ACTIVITY OF BACTERIAL COMMUNITY OF COAL TAILING." Biotechnologia acta 7, no. 5 (2014): 94–100. http://dx.doi.org/10.15407/biotech7.05.094.

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2

Özbayram, E. Gözde, Latife Köker, Reyhan Akçaalan, Orhan İnce, and Meriç Albay. "Bacterial Community Composition of Sapanca Lake During a Cyanobacterial Bloom." Aquatic Sciences and Engineering 35, no. 2 (March 16, 2020): 52–56. http://dx.doi.org/10.26650/ase2020652073.

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3

Mereta, Seid Tiku, Pieter Lemmens, Luc De Meester, Peter L. M. Goethals, and Pieter Boets. "The Relative Importance of Human Disturbance, Environmental and Spatial Factors on the Community Composition of Wetland Birds." Water 13, no. 23 (December 4, 2021): 3448. http://dx.doi.org/10.3390/w13233448.

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The present study investigates the relative importance of human disturbance, local environmental and spatial factors on variations in bird community composition in natural Ethiopian wetlands with high biodiversity conservation value. We quantified bird abundances, local environmental variables and human disturbances at 63 sites distributed over ten wetlands in two subsequent years. Variation partitioning analyses were used to explore the unique and shared contributions of human disturbance, local environmental variables and spatial factors on variations in community compositions of wetland bird species. Local environmental variables explained the largest amount of compositional variation of wetland bird species. Productivity-related variables were the most important local environmental variables determining bird community composition. Human disturbance was also an important determinant for wetland bird community composition and affected the investigated communities mainly indirectly through its effect on local environmental conditions. Spatial factors only played a minor role in variations in bird community composition. Our study highlights the urgent need for integrated management approaches that consider both nature conservation targets and socio-economic development of the region for the sustainable use and effective conservation of wetland resources.
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4

Johnson Gaither, Cassandra. "Smokestacks, Parkland, and Community Composition." Environment and Behavior 47, no. 10 (September 2, 2014): 1127–46. http://dx.doi.org/10.1177/0013916514546744.

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5

Kyselková, M., J. Kopecký, M. Ságová-Marečková, G. L. Grundmann, and Y. Moënne-Loccoz. "Oligonucleotide microarray methodology for taxonomic and functional monitoringof microbial community composition." Plant, Soil and Environment 55, No. 9 (October 14, 2009): 379–88. http://dx.doi.org/10.17221/140/2009-pse.

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Microarray analysis is a cultivation-independent, high-throughput technology that can be used for direct and simultaneous identification of microorganisms in complex environmental samples. This review summarizes current methodologies for oligonucleotide microarrays used in microbial ecology. It deals with probe design, microarray manufacturing, sample preparation and labeling, and data handling, as well as with the key features of microarray analysis such as specificity, sensitivity and quantification potential. Microarray analysis has been validated as an effective approach to describe the composition and dynamics of taxonomic and functional microbial communities, in environments including soil, compost, sediment, air or humans. It is now part of the technical arsenal available to address key issues in microbial community ecology, ranging from biogeography to ecosystem functioning.
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6

Geigle, Chase, Himel Dev, Hari Sundaram, and ChengXiang Zhai. "A Generative Model for Discovering Action-Based Roles and Community Role Compositions on Community Question Answering Platforms." Proceedings of the International AAAI Conference on Web and Social Media 13 (July 6, 2019): 181–92. http://dx.doi.org/10.1609/icwsm.v13i01.3220.

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This paper proposes a generative model for discovering user roles and community role compositions in Community Question Answering (CQA) platforms. While past research shows that participants play different roles in online communities, automatically discovering these roles and providing a summary of user behavior that is readily interpretable remains an important challenge. Furthermore, there has been relatively little insight into the distribution of these roles between communities. Does a community’s composition over user roles vary as a function of topic? How does it relate to the health of the underlying community? Does role composition evolve over time? The generative model proposed in this paper, the mixture of Dirichlet-multinomial mixtures (MDMM) behavior model can (1) automatically discover interpetable user roles (as probability distributions over atomic actions) directly from log data, and (2) uncover community-level role compositions to facilitate such cross-community studies. A comprehensive experiment on all 161 non-meta communities on the StackExchange CQA platform demonstrates that our model can be useful for a wide variety of behavioral studies, and we highlight three empirical insights. First, we show interesting distinctions in question-asking behavior on StackExchange (where two distinct types of askers can be identified) and answering behavior (where two distinct roles surrounding answers emerge). Second, we find statistically significant differences in behavior compositions across topical groups of communities on StackExchange, and that those groups that have statistically significant differences in health metrics also have statistically significant differences in behavior compositions, suggesting a relationship between behavior composition and health. Finally, we show that the MDMM behavior model can be used to demonstrate similar but distinct evolutionary patterns between topical groups.
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7

Zhou, Baoqing, Jen Mobberley, Kelly Shi, and Irene A. Chen. "Effects of Preservation and Propagation Methodology on Microcosms Derived from the Oral Microbiome." Microorganisms 10, no. 11 (October 29, 2022): 2146. http://dx.doi.org/10.3390/microorganisms10112146.

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The creation of oral microcosms with reproducible composition is important for developing model systems of the oral microbiome. However, oral microbiomes vary substantially across individuals. To derive a reproducible composition from inocula sourced from different individuals, we tested whether selective conditions from cold storage and culturing in defined media would generate a reproducible community composition despite individual variations. In this pilot study, we collected dental plaque scrapings from three individuals, inoculated media under anaerobic conditions, and characterized the bacterial community compositions after cold storage and subsequent propagation in liquid media. Harvested cultures were extracted and bacterial composition was determined by 16S rRNA gene amplicon sequencing and the mothur pipeline. Our results show that samples from two out of three individuals clustered into a specific compositional type (termed “attractor” here). In addition, the samples from the third individual could adopt this attractor compositional type after propagation in vitro, even though its original composition did not display this type. These results indicate that simple selective environments could help create reproducible microcosms despite variation among dental plaque samples sourced from different individuals. The findings illustrate important parameters to consider for creating reproducible microcosms from the human oral microbiome.
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8

Wimp, Gina M., and Shannon M. Murphy. "Habitat edges alter arthropod community composition." Landscape Ecology 36, no. 10 (July 4, 2021): 2849–61. http://dx.doi.org/10.1007/s10980-021-01288-6.

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9

Hitchcock, James N. "Microplastics can alter phytoplankton community composition." Science of The Total Environment 819 (May 2022): 153074. http://dx.doi.org/10.1016/j.scitotenv.2022.153074.

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10

Kozhurina, V. M. "COMPOSITION OF TERRITORIAL COMMUNITY LAND RESOURCES." Dnipro Scientific Journal of Public Administration, Psychology, Law, no. 2 (2021): 114–18. http://dx.doi.org/10.51547/ppp.dp.ua/2021.2.20.

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11

Kempel, Anne, Mialy Razanajatovo, Claudia Stein, Sybille B. Unsicker, Harald Auge, Wolfgang W. Weisser, Markus Fischer, and Daniel Prati. "Herbivore preference drives plant community composition." Ecology 96, no. 11 (November 2015): 2923–34. http://dx.doi.org/10.1890/14-2125.1.

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12

de Vries, Jip, Michiel H. S. Kraak, Ralf C. M. Verdonschot, and Piet F. M. Verdonschot. "Species composition drives macroinvertebrate community classification." Ecological Indicators 119 (December 2020): 106780. http://dx.doi.org/10.1016/j.ecolind.2020.106780.

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13

de Boer, Wietse, Patrick Verheggen, Paulien J. A. Klein Gunnewiek, George A. Kowalchuk, and Johannes A. van Veen. "Microbial Community Composition Affects Soil Fungistasis." Applied and Environmental Microbiology 69, no. 2 (February 2003): 835–44. http://dx.doi.org/10.1128/aem.69.2.835-844.2003.

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ABSTRACT Most soils inhibit fungal germination and growth to a certain extent, a phenomenon known as soil fungistasis. Previous observations have implicated microorganisms as the causal agents of fungistasis, with their action mediated either by available carbon limitation (nutrient deprivation hypothesis) or production of antifungal compounds (antibiosis hypothesis). To obtain evidence for either of these hypotheses, we measured soil respiration and microbial numbers (as indicators of nutrient stress) and bacterial community composition (as an indicator of potential differences in the composition of antifungal components) during the development of fungistasis. This was done for two fungistatic dune soils in which fungistasis was initially fully or partly relieved by partial sterilization treatment or nutrient addition. Fungistasis development was measured as restriction of the ability of the fungi Chaetomium globosum, Fusarium culmorum, Fusarium oxysporum, and Trichoderma harzianum to colonize soils. Fungistasis did not always reappear after soil treatments despite intense competition for carbon, suggesting that microbial community composition is important in the development of fungistasis. Both microbial community analysis and in vitro antagonism tests indicated that the presence of pseudomonads might be essential for the development of fungistasis. Overall, the results lend support to the antibiosis hypothesis.
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14

Seabloom, Eric W., Elizabeth T. Borer, Kevin Gross, Amy E. Kendig, Christelle Lacroix, Charles E. Mitchell, Erin A. Mordecai, and Alison G. Power. "The community ecology of pathogens: coinfection, coexistence and community composition." Ecology Letters 18, no. 4 (March 2, 2015): 401–15. http://dx.doi.org/10.1111/ele.12418.

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15

Dolbeth, Marina, Heliana Teixeira, João Carlos Marques, and Miguel Ângelo Pardal. "Feeding guild composition of a macrobenthic subtidal community along a depth gradient." Scientia Marina 73, no. 2 (March 16, 2009): 225–37. http://dx.doi.org/10.3989/scimar.2009.73n2225.

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16

Snoeijs, P. J. M. "Monitoring pollution effects by diatom community composition. A comparison of sampling methods." Archiv für Hydrobiologie 121, no. 4 (August 13, 1991): 497–510. http://dx.doi.org/10.1127/archiv-hydrobiol/121/1991/497.

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17

Lourdes, Vital, Narvaez Jose A, Cruz Maria Antonia, Ortiz Eyra L, Sanchez Eric, and Mendoza Alberto. "Unravelling the composition of soil belowground microbial community before sowing transgenic cotton." Plant, Soil and Environment 63, No. 11 (November 20, 2017): 512–18. http://dx.doi.org/10.17221/523/2017-pse.

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Soils harbour enormously diverse bacterial communities that interact specifically with plants generating beneficial interactions between them. This study was the first approach to assess bacterial communities before sowing with three cotton genotypes, including both transgenic and conventional ones. The structure of bacterial communities was identified using the next generation sequencing analysis, ion torrent PGM (Personal Genome Machine™) sequencer technology, based on the V2–V3 16S rRNA gene region. Quantitative insights into microbial ecology pipeline were used to identify the structure and diversity of bacterial communities in bulk soil samples collected in the northeast of Mexico. Bulk soil textures and chemical properties, including most nutrients, were homogeneous in these bulk soil samples. Relative abundance analysis showed similar bacterial community structures. Dominant taxonomic phyla were Proteobacteria, Firmicutes, Acidobacteria, Actinobacteria, Gemmatimonadetes and Bacteroidetes, whereas the main families were Bacillaceae, Chitinophagaceae and Rhodospirillaceae with an abundance average of BS1 (bulk soil sample), BS2 and BS3 (24.85, 19.74 and 19.71%, respectively). Alpha diversity analysis showed a high diversity (Shannon and Simpson index) and a large value of the observed species found in bulk soils samples. These results allowed establishing the previous bacterial structural community in an unused soil before sowing it with a transgenic crop for the first time.
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18

Czerwik-Marcinkowska, Joanna, Anna Wojciechowska, Teresa Mrozińska, and Agata Wojtal. "Algal diversity and community composition of peat bogs in Poland (Central Europe)." Phytocoenologia 49, no. 3 (October 29, 2019): 249–62. http://dx.doi.org/10.1127/phyto/2019/0278.

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19

Holopainen, Risto. "Making Complex Music with Simple Algorithms, is it Even Possible?" Revista Vórtex 9, no. 2 (December 10, 2021): 1–13. http://dx.doi.org/10.33871/23179937.2021.9.2.3.

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Algorithmic composition is often limited to score generation, but may also include sound production. All levels from sound synthesis to the generation of a complete composition can be integrated into one monolithic program. A strict separation of the low level of sound synthesis and higher levels otherwise reserved for algorithmic composition is not necessary, information can flow between all levels. An interesting challenge in this kind of thorough algorithmic composition is to generate as complex music as possible with as little code as possible. The challenge has been accepted, successfully or not, in a series of compositions called Kolmogorov Variations. We discuss the techniques used in a few of the pieces as well as the promises and perils of this strict approach to algorithmic composition.
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20

Worthen, Wade B. "Community Composition and Nested-Subset Analyses: Basic Descriptors for Community Ecology." Oikos 76, no. 3 (September 1996): 417. http://dx.doi.org/10.2307/3546335.

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21

Niu, Yuan, Hong Shen, Jun Chen, Ping Xie, Xi Yang, Min Tao, Zhimei Ma, and Min Qi. "Phytoplankton community succession shaping bacterioplankton community composition in Lake Taihu, China." Water Research 45, no. 14 (August 2011): 4169–82. http://dx.doi.org/10.1016/j.watres.2011.05.022.

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22

Zhang, Hui, Wanhui Ye, and Juyu Lian. "Scale-Dependent Functional Redundancy in a Tropical Forest." Tropical Conservation Science 12 (January 2019): 194008291989385. http://dx.doi.org/10.1177/1940082919893853.

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Functional redundancy is an important tool for justifying and prioritizing species protection in forest ecosystem, but it is a scale-dependent. If functional redundancy really exists, functional trait composition tends to have higher predictive ability of community assembly than species composition. Thus, comparing the differences in the predictive ability of community assembly between species and functional trait compositions across spatial scale represents a useful tool to quantify how functional redundancy varies across spatial scales. Here, we used variation partitioning in combination with distance-based Moran’s eigenvector maps to compare the differences in the predictive ability of community assembly between species composition and functional trait composition across spatial scales (20, 30, 40, 50, and 100 m) in a 20-ha subtropical forest plot. We found that functional trait composition possessed higher predictive ability of niche-based abiotic filtering process than species composition within 40 m. At 50 and 100 m scales, both species and functional trait compositions had approximately equal predictive ability of dispersal limitation processes. Thus, functional redundancy can only exist within 40 m scale but not 50 and 100 m scales. As a result, priority species loss protection should be performed at 50 and 100 m scales.
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23

Fried, Oliver, Ingolf Kühn, Julian Schrader, Van Sinh Nguyen, and Erwin Bergmeier. "Plant diversity and community composition of rice agroecosystems in Vietnam and the Philippines." Phytocoenologia 47, no. 1 (January 1, 2017): 49–66. http://dx.doi.org/10.1127/phyto/2017/0123.

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24

Chan, Alfred, Madiha Naseem, Mykola Onyshchenko, and Delphine Lee. "Bacterial community composition in breast cancer subtypes." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e13063-e13063. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e13063.

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e13063 Background: Breast cancer is a heterogeneous disease with multiple molecular subtypes and prognostic outcomes. Among these subtypes, Luminal-A is enriched with estrogen receptor and has the best clinical prognosis, whereas triple-negative breast cancer (TNBC) remains an aggressive subtype with poor prognosis. Changes in the diversity of breast microbiome have been associated with breast cancer initiation and progression. We hypothesized that Luminal-A and TNBC subtypes will have differences in both bacterial community composition as well as differences in microbial genomes and pathways. Methods: 6 Luminal-A and 6 TNBC FPPE tissue blocks were processed for both 16S V4 rRNA amplicon sequencing and shotgun metagenomics sequencing. 16S sequencing data was processed using mothur (v1.44.3) and reads were assigned taxonomy against the SILVA 16S rRNA database (v132). Shotgun sequencing was processed using the EBI-Metagenomic (v5) pipeline; bacterial identification was performed using the Kraken pipeline. Results: Shannon alpha-diversity or species enrichment, was significantly higher in Luminal-A compared to TNBC (nonparametric, P = 0.008) and their microbiome composition was significantly different by beta-diversity analysis (bray-curtis dissimilarity, PERMANOVA, P = 0.014). Bacteroides and Sphingomonadaceae species were significantly higher in abundance in Luminal-A, compared to Pseudomonas and Neisseriaceae species abundance in TNBC. Results were validated by shotgun sequencing. Luminal-A and TNBC bacterial metabolic pathways were not statistically different overall (PERMANOVA, P = 0.312). Pathways associated with Luminal-A included Response to Wounding (GO: 0009611), tRNA modification (GO: 0006400), and Cytoplasmic Microtubule Organization (GO: 0031122). Pathways associated with TNBC included troponin complex (GO: 0005861), rRNA processing (GO: 0006364), and CDC73 PAF1 (GO: 0016593). Conclusions: Using both shotgun and amplicon sequencing, this is the first analysis to show that Luminal-A and TNBC subtypes have statistically significant differences in bacterial species composition. Luminal-A had higher species diversity and abundance of Sphingomonadaceae, which are associated with healthy breast tissue and better clinical outcomes. TNBC had lower species diversity and abundance of Pseudomonas and Neisseriaceae, which have been associated with tumor survival and cell growth. Understanding the role of microbiota and its complex interaction with tumor microenvironment could help identify new treatment targets in breast cancer subtypes, and warrants further investigations.
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Chan, Alfred, Madiha Naseem, Mykola Onyshchenko, and Delphine Lee. "Bacterial community composition in breast cancer subtypes." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): e13063-e13063. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.e13063.

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e13063 Background: Breast cancer is a heterogeneous disease with multiple molecular subtypes and prognostic outcomes. Among these subtypes, Luminal-A is enriched with estrogen receptor and has the best clinical prognosis, whereas triple-negative breast cancer (TNBC) remains an aggressive subtype with poor prognosis. Changes in the diversity of breast microbiome have been associated with breast cancer initiation and progression. We hypothesized that Luminal-A and TNBC subtypes will have differences in both bacterial community composition as well as differences in microbial genomes and pathways. Methods: 6 Luminal-A and 6 TNBC FPPE tissue blocks were processed for both 16S V4 rRNA amplicon sequencing and shotgun metagenomics sequencing. 16S sequencing data was processed using mothur (v1.44.3) and reads were assigned taxonomy against the SILVA 16S rRNA database (v132). Shotgun sequencing was processed using the EBI-Metagenomic (v5) pipeline; bacterial identification was performed using the Kraken pipeline. Results: Shannon alpha-diversity or species enrichment, was significantly higher in Luminal-A compared to TNBC (nonparametric, P = 0.008) and their microbiome composition was significantly different by beta-diversity analysis (bray-curtis dissimilarity, PERMANOVA, P = 0.014). Bacteroides and Sphingomonadaceae species were significantly higher in abundance in Luminal-A, compared to Pseudomonas and Neisseriaceae species abundance in TNBC. Results were validated by shotgun sequencing. Luminal-A and TNBC bacterial metabolic pathways were not statistically different overall (PERMANOVA, P = 0.312). Pathways associated with Luminal-A included Response to Wounding (GO: 0009611), tRNA modification (GO: 0006400), and Cytoplasmic Microtubule Organization (GO: 0031122). Pathways associated with TNBC included troponin complex (GO: 0005861), rRNA processing (GO: 0006364), and CDC73 PAF1 (GO: 0016593). Conclusions: Using both shotgun and amplicon sequencing, this is the first analysis to show that Luminal-A and TNBC subtypes have statistically significant differences in bacterial species composition. Luminal-A had higher species diversity and abundance of Sphingomonadaceae, which are associated with healthy breast tissue and better clinical outcomes. TNBC had lower species diversity and abundance of Pseudomonas and Neisseriaceae, which have been associated with tumor survival and cell growth. Understanding the role of microbiota and its complex interaction with tumor microenvironment could help identify new treatment targets in breast cancer subtypes, and warrants further investigations.
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26

Hu, Patrick J. "Microbiome: Insulin signaling shapes gut community composition." Current Biology 31, no. 12 (June 2021): R803—R806. http://dx.doi.org/10.1016/j.cub.2021.05.027.

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27

Zheng, Jie, Muhammad Arif, Songlin Zhang, Zhongxun Yuan, Limiao Zhang, Jiajia Li, Dongdong Ding, and Changxiao Li. "Dam inundation simplifies the plant community composition." Science of The Total Environment 801 (December 2021): 149827. http://dx.doi.org/10.1016/j.scitotenv.2021.149827.

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28

McCulley, R. L., and I. C. Burke. "Microbial Community Composition across the Great Plains." Soil Science Society of America Journal 68, no. 1 (2004): 106. http://dx.doi.org/10.2136/sssaj2004.0106.

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McCulley, R. L., and I. C. Burke. "Microbial Community Composition across the Great Plains." Soil Science Society of America Journal 68, no. 1 (January 2004): 106–15. http://dx.doi.org/10.2136/sssaj2004.1060.

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30

Drake, Dana L., Brittany H. Ousterhout, Jarrett R. Johnson, Thomas L. Anderson, William E. Peterman, Christopher D. Shulse, Daniel J. Hocking, et al. "Pond-Breeding Amphibian Community Composition in Missouri." American Midland Naturalist 174, no. 1 (July 2015): 180–87. http://dx.doi.org/10.1674/0003-0031-174.1.180.

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31

Hoschitz, Michael, and Rüdiger Kaufmann. "Nematode community composition in five alpine habitats." Nematology 6, no. 5 (2004): 737–47. http://dx.doi.org/10.1163/1568541042843531.

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AbstractSoil nematodes were studied in five alpine habitats (sedge mat, pasture, peat bog, moraine, and lichen heath) in the Austrian Alps (Obergurgl, Tyrol) from 2001 to 2002. A total of 75 genera within 49 families were found. The mean nematode population density ranged from 80 to 383 individuals 100 g–1 soil and increased during the short alpine growing season. The most abundant feeding groups were bacterial feeders followed by plant-parasitic nematodes. The number of nematode taxa differed only slightly between habitats. Diversity indices (H' =3.7-4.8 based on genera) and maturity indices (MI = 2.7-3.4) were generally high. Nematode community composition of the pasture and the peat bog differed markedly from the other three sites. Nematode data on genera and feeding types yielded a comparable similarity pattern for all sites but site discrimination was better at the genus level.
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32

Fazi, Stefano, Eusebi Vázquez, Emilio O. Casamayor, Stefano Amalfitano, and Andrea Butturini. "Stream Hydrological Fragmentation Drives Bacterioplankton Community Composition." PLoS ONE 8, no. 5 (May 31, 2013): e64109. http://dx.doi.org/10.1371/journal.pone.0064109.

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Shafer, Gregory. "Composition and a Prison Community of Writers." English Journal 90, no. 5 (May 2001): 75. http://dx.doi.org/10.2307/821858.

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34

Sandau, Nadine, Rudolf P. Rohr, Russell E. Naisbit, Yvonne Fabian, Odile T. Bruggisser, Patrik Kehrli, Alexandre Aebi, and Louis-Félix Bersier. "Including community composition in biodiversity-productivity models." Methods in Ecology and Evolution 5, no. 8 (July 12, 2014): 815–23. http://dx.doi.org/10.1111/2041-210x.12215.

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35

Morlon, Hélène, Sonia Kefi, and Neo D. Martinez. "Effects of trophic similarity on community composition." Ecology Letters 17, no. 12 (October 7, 2014): 1495–506. http://dx.doi.org/10.1111/ele.12356.

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36

Cardoso, Isabella Sichierski, Adriano Jakelaitis, Michellia Pereira Soares, Vinícius Tavares de Araújo, and Paulo Henrique Ramos Cabral. "Weed community composition in different agro-systems." Comunicata Scientiae 8, no. 1 (April 6, 2017): 139. http://dx.doi.org/10.14295/cs.v8i1.1451.

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Weed communities in agricultural systems are diverse and subjected to different agronomic practices. In this study, a phytosociological survey of the weed community in different cropping systems consisting of corn, horticulture, perennial cultivation of Jatropha curcas and pasture of Urochloa brizantha in Rio Verde, GO, Brazil, was carried out. Assessments were performed at the beginning (October 2011) and at the end (March 2012) of the rainy season. After identifying and counting species, were calculated the phytosociological indices of density, frequency, abundance and relative importance, as well as the diversity index of Shannon-Wiener, evenness index of Pielou and similarity coefficient of Sorense. Were found 46 species of sixteen families, especially Poaceae. The most important species were: Alternanthera tenella in the annual growth environment during the both periods; Panicum maximum (early rainy season) and Chamaescy hirta (end of the rainy season) in the vegetable garden; Eleusine indica (early rainy season) and Galinsoga parviflora (end of the rainy season) in the perennial crop and Sida rhombifolia (early rainy season) and Neonotonia wigrtii (end of the rainy season) in the pasture. Lower diversity indices and evenness were observed in the pasture environment. Sampling time presented a higher impact on similarities when compared to the different environments.
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37

Mhuireach, Gwynne, Bart R. Johnson, Adam E. Altrichter, Joshua Ladau, James F. Meadow, Katherine S. Pollard, and Jessica L. Green. "Urban greenness influences airborne bacterial community composition." Science of The Total Environment 571 (November 2016): 680–87. http://dx.doi.org/10.1016/j.scitotenv.2016.07.037.

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38

Payne, Richard J., Simon J. M. Caporn, Carly J. Stevens, Jacky A. Carroll, Jill L. Edmondson, David J. Gowing, and Nancy B. Dise. "Inferring nitrogen deposition from plant community composition." Ecological Indicators 26 (March 2013): 1–4. http://dx.doi.org/10.1016/j.ecolind.2012.10.013.

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39

Hooker, E., M. Ruiz, and M. Pum. "Phytoplankton community composition in Lake Xolotlán (Managua)." Hydrobiological Bulletin 25, no. 2 (June 1991): 121–24. http://dx.doi.org/10.1007/bf02291242.

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40

Gieselman, Tanis M., Karen E. Hodges, and Mark Vellend. "Human-induced edges alter grassland community composition." Biological Conservation 158 (February 2013): 384–92. http://dx.doi.org/10.1016/j.biocon.2012.08.019.

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HANSSON, LARS-ANDERS, SUSANNE GUSTAFSSON, KARIN RENGEFORS, and LINA BOMARK. "Cyanobacterial chemical warfare affects zooplankton community composition." Freshwater Biology 52, no. 7 (July 2007): 1290–301. http://dx.doi.org/10.1111/j.1365-2427.2007.01765.x.

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Collins, C. D., C. Banks-Leite, L. A. Brudvig, B. L. Foster, W. M. Cook, E. I. Damschen, A. Andrade, et al. "Fragmentation affects plant community composition over time." Ecography 40, no. 1 (December 14, 2016): 119–30. http://dx.doi.org/10.1111/ecog.02607.

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Melkun, Cheryl Hawkinson. "Nontraditional Students Online: Composition, Collaboration, and Community." Journal of Continuing Higher Education 60, no. 1 (January 2012): 33–39. http://dx.doi.org/10.1080/07377363.2012.649128.

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Bondoso, Joana, Vanessa Balagué, Josep M. Gasol, and Olga M. Lage. "Community composition of thePlanctomycetesassociated with different macroalgae." FEMS Microbiology Ecology 88, no. 3 (May 12, 2014): 445–56. http://dx.doi.org/10.1111/1574-6941.12258.

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Hodges, Mackenzie N., and Michael L. McKinney. "Urbanization impacts on land snail community composition." Urban Ecosystems 21, no. 4 (February 27, 2018): 721–35. http://dx.doi.org/10.1007/s11252-018-0746-x.

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Kemp, W. P., S. J. Harvey, and K. M. O'Neill. "Patterns of vegetation and grasshopper community composition." Oecologia 83, no. 3 (June 1990): 299–308. http://dx.doi.org/10.1007/bf00317552.

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van der Linde, Sietse, and Simone Haller. "Obtaining a spore free fungal community composition." Fungal Ecology 6, no. 6 (December 2013): 522–26. http://dx.doi.org/10.1016/j.funeco.2013.10.001.

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Holdridge, Erica M., Gilberto E. Flores, and Casey P. terHorst. "Predator trait evolution alters prey community composition." Ecosphere 8, no. 5 (May 2017): e01803. http://dx.doi.org/10.1002/ecs2.1803.

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Cao, Yiping, Peter G. Green, and Patricia A. Holden. "Microbial Community Composition and Denitrifying Enzyme Activities in Salt Marsh Sediments." Applied and Environmental Microbiology 74, no. 24 (October 31, 2008): 7585–95. http://dx.doi.org/10.1128/aem.01221-08.

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Abstract:
ABSTRACT Denitrifying microbial communities and denitrification in salt marsh sediments may be affected by many factors, including environmental conditions, nutrient availability, and levels of pollutants. The objective of this study was to examine how microbial community composition and denitrification enzyme activities (DEA) at a California salt marsh with high nutrient loading vary with such factors. Sediments were sampled from three elevations, each with different inundation and vegetation patterns, across 12 stations representing various salinity and nutrient conditions. Analyses included determination of cell abundance, total and denitrifier community compositions (by terminal restriction fragment length polymorphism), DEA, nutrients, and eluted metals. Total bacterial (16S rRNA) and denitrifier (nirS) community compositions and DEA were analyzed for their relationships to environmental variables and metal concentrations via multivariate direct gradient and regression analyses, respectively. Community composition and DEA were highly variable within the dynamic salt marsh system, but each was strongly affected by elevation (i.e., degree of inundation) and carbon content as well as by selected metals. Carbon content was highly related to elevation, and the relationships between DEA and carbon content were found to be elevation specific when evaluated across the entire marsh. There were also lateral gradients in the marsh, as evidenced by an even stronger association between community composition and elevation for a marsh subsystem. Lastly, though correlated with similar environmental factors and selected metals, denitrifier community composition and function appeared uncoupled in the marsh.
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Courtney, Ronan, Jim A. Harris, and Mark Pawlett. "Microbial Community Composition in a Rehabilitated Bauxite Residue Disposal Area: A Case Study for Improving Microbial Community Composition." Restoration Ecology 22, no. 6 (August 5, 2014): 798–805. http://dx.doi.org/10.1111/rec.12143.

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